The Development of Educational Technology Policies (1996-2012) Lessons from China and the USA
Bibliographic record
Abstract
This study reviews the development of educational technology macro policies in China and USA based on the historical juxtaposition approach. It shows that, despite the fact that two countries have major differences, with China officially being a socialist country, while the USA is a capitalist country; the development of educational technology policies in the two countries has displayed remarkable similarities. USA’s revised educational technology plans also were used as the basis of some of Chinese educational technology policies. The study displays how the ambitions toward integrating ICT in education moved from critical issues facing elementary and secondary schools in the USA like technology literacy after 1990, to adopting a technology-based learning model that resulted in measuring of student’s outcomes individually in NETP 2010. China has moved from using traditional technology in education in the early 1990 e.g. slides, projectors, films, and radio and aims at building an educational infrastructure close to the developed countries by 2020. The study reviews briefly the development of equipping American and Chinese schools with the ICT hardware infrastructure and the educational technology standards in both countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".